**Matt's Stats n stuff ยป R**, and kindly contributed to R-bloggers)

Nothing ground breaking here. I was doing some work dividing data into deciles and then creating some plots. I couldn’t find an function to calculate this from cut, and I use cut quite a bit. So here we are.

midpoints <- function(x, dp=2){

lower <- as.numeric(gsub(“,.*”,””,gsub(“\\(|\\[|\\)|\\]“,””, x)))

upper <- as.numeric(gsub(“.*,”,””,gsub(“\\(|\\[|\\)|\\]“,””, x)))

return(round(lower+(upper-lower)/2, dp))

}

And in an example:

midpoints <- function(x, dp=2){

lower <- as.numeric(gsub(“,.*”,””,gsub(“\\(|\\[|\\)|\\]“,””, x)))

upper <- as.numeric(gsub(“.*,”,””,gsub(“\\(|\\[|\\)|\\]“,””, x)))

return(round(lower+(upper-lower)/2, dp))

}

mtcars$mpg

cut(mtcars$mpg, quantile(mtcars$mpg), include.lowest=T)

midpoints(cut(mtcars$mpg, quantile(mtcars$mpg), include.lowest=T))

Which looks like this:

> midpoints <- function(x, dp=2){

+ย ย lower <- as.numeric(gsub(“,.*”,””,gsub(“\\(|\\[|\\)|\\]“,””, x)))

+ย ย upper <- as.numeric(gsub(“.*,”,””,gsub(“\\(|\\[|\\)|\\]“,””, x)))

+ย ย return(round(lower+(upper-lower)/2, dp))

+ }

>

> mtcars$mpg

[1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 10.4 14.7

[18] 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 15.0 21.4

> cut(mtcars$mpg, quantile(mtcars$mpg), include.lowest=T)

[1] (19.2,22.8] (19.2,22.8] (19.2,22.8] (19.2,22.8] (15.4,19.2] (15.4,19.2] [10.4,15.4]

[8] (22.8,33.9] (19.2,22.8] (15.4,19.2] (15.4,19.2] (15.4,19.2] (15.4,19.2] [10.4,15.4]

[15] [10.4,15.4] [10.4,15.4] [10.4,15.4] (22.8,33.9] (22.8,33.9] (22.8,33.9] (19.2,22.8]

[22] (15.4,19.2] [10.4,15.4] [10.4,15.4] (15.4,19.2] (22.8,33.9] (22.8,33.9] (22.8,33.9]

[29] (15.4,19.2] (19.2,22.8] [10.4,15.4] (19.2,22.8]

Levels: [10.4,15.4] (15.4,19.2] (19.2,22.8] (22.8,33.9]

> midpoints(cut(mtcars$mpg, quantile(mtcars$mpg), include.lowest=T))

[1] 21.00 21.00 21.00 21.00 17.30 17.30 12.90 28.35 21.00 17.30 17.30 17.30 17.30 12.90

[15] 12.90 12.90 12.90 28.35 28.35 28.35 21.00 17.30 12.90 12.90 17.30 28.35 28.35 28.35

[29] 17.30 21.00 12.90 21.00

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